3061 results about "Target text" patented technology

A method and apparatus for aligning texts. The method includes acquiring a target text and a reference text and aligning the target text and the reference text at word level based on phoneme similarity. The method can be applied to automatically archiving a multimedia resource and a method of automatically searching a multimedia resource.

A system and method for hybrid machine translation approach is based on a statistical transfer approach using statistical and linguistic features. The system and method may be used to translate from one language into another. The system may include at least one database, a rule based translation module, a statistical translation module and a hybrid machine translation engine. The database(s) store source and target text and rule based language models and statistical language models. The rule based translation module translates source text based on the rule based language models. The statistical translation module translates source text based on the statistical language models. A hybrid machine translation engine, having a maximum entropy algorithm, is coupled to the rule based translation module and the statistical translation module and is capable of translating source text into target text based on the rule based and statistical language models.

A computer implemented method of translating source material in a source natural language into a target natural language includes receiving a first data input which is a first part of a sub-segment of a translation of the source material from the source natural language into the target natural language, identifying a selectable target text sub-segment in the target natural language associated with the received first data input, and outputting the selectable target text sub-segment. The selectable target text sub-segment is extracted from a corpus of previously translated text segment pairs, each text segment pair having a source text segment in the source natural language and a corresponding translated text segment in the target natural language.

The invention claims to protect a character recognition system and method based on the combination of a neural network and an attention mechanism, the system comprising: a convolution neural network feature extraction module, which is used for spatial feature of character image; The spatial features extracted by the convolution neural network are input to the bi-directional long-short memory network module, and the bi-directional long-short memory network can extract the sequence features of characters. The extracted feature vectors are semantically encoded, and then the attention weights of feature vectors are assigned through the attention mechanism, so that the attention is focused on the feature vectors with higher weights. In the decoding part of the model, the features extracted fromattention and the prediction information of the previous time are used as the inputs of the nested long-short memory network. The purpose of using the long-short memory network is to keep the temporal characteristics of the eigenvectors and make the attention points of the model constantly change with time. In the decoding part, the features extracted from attention and the prediction informationof the previous time are used as the inputs of the nested long-short memory network. The invention can more accurately detect the text area in the natural scene, and has good detection effect on thesmall target text and the text with small tilt angle.

The invention discloses an automatic question-answer processing method and an automatic question-answer system. The method includes: acquiring question text from question-answer data pairs collected in advance, performing word separation on the question text to obtain the corresponding key words of the question text, and building the index relation between the key words and the question text; whenoptional target question text is received, and performing word separation on the target question text to acquire target key words corresponding to the target text question text; according to the index relation of the key words and the question text, determining key words matched with the target key words, and acquiring the question text having index relation with the key words to serve as the candidate question text; calculating the semantic similarity of the candidate question text and the target question text; determining an answer corresponding to the target question text according to thesemantic similarity. The method has the advantages that the semantic similarity of the target question text and each question text is considered to determine the answer of the target question text, and the accuracy of automatic question-answer processing is increased.